import logging
import time
import torch
import psutil # Ensure psutil is imported here as well

logger = logging.getLogger(__name__)

class EnsembleMonitorAgent:
    def __init__(self):
        self.performance_metrics = {
            "model_accuracy": {},
            "response_times": {},
            "confidence_distribution": {},
            "consensus_rate": 0.0
        }
        self.alerts = []
    
    def monitor_prediction(self, model_id, prediction, confidence, response_time):
        """Monitor individual model performance"""
        if model_id not in self.performance_metrics["model_accuracy"]:
            self.performance_metrics["model_accuracy"][model_id] = []
            self.performance_metrics["response_times"][model_id] = []
            self.performance_metrics["confidence_distribution"][model_id] = []
        
        self.performance_metrics["response_times"][model_id].append(response_time)
        self.performance_metrics["confidence_distribution"][model_id].append(confidence)
        
        # Check for performance issues
        self._check_performance_issues(model_id)
    
    def _check_performance_issues(self, model_id):
        """Check for any performance anomalies"""
        response_times = self.performance_metrics["response_times"][model_id]
        if len(response_times) > 10:
            avg_time = sum(response_times[-10:]) / 10
            if avg_time > 2.0:  # More than 2 seconds
                self.alerts.append(f"High latency detected for {model_id}: {avg_time:.2f}s")

class WeightOptimizationAgent:
    def __init__(self, weight_manager):
        self.weight_manager = weight_manager
        self.prediction_history = []  # Stores (ensemble_prediction_label, assumed_actual_label)
        self.optimization_threshold = 0.05  # 5% change in accuracy triggers optimization
        self.min_history_for_optimization = 20 # Minimum samples before optimizing
    
    def analyze_performance(self, ensemble_prediction_label, actual_label=None):
        """Analyze ensemble performance and record for optimization"""
        # If actual_label is not provided, assume ensemble is correct if not UNCERTAIN
        assumed_actual_label = actual_label
        if assumed_actual_label is None and ensemble_prediction_label != "UNCERTAIN":
            assumed_actual_label = ensemble_prediction_label
        
        self.prediction_history.append((ensemble_prediction_label, assumed_actual_label))
        
        if len(self.prediction_history) >= self.min_history_for_optimization and self._should_optimize():
            self._optimize_weights()
    
    def _calculate_accuracy(self, history_subset):
        """Calculates accuracy based on history where actual_label is known."""
        correct_predictions = 0
        total_known = 0
        for ensemble_pred, actual_label in history_subset:
            if actual_label is not None:
                total_known += 1
                if ensemble_pred == actual_label:
                    correct_predictions += 1
        return correct_predictions / total_known if total_known > 0 else 0.0

    def _should_optimize(self):
        """Determine if weights should be optimized based on recent performance change."""
        if len(self.prediction_history) < self.min_history_for_optimization * 2: # Need enough history for comparison
            return False
        
        # Compare accuracy of recent batch with previous batch
        recent_batch = self.prediction_history[-self.min_history_for_optimization:]
        previous_batch = self.prediction_history[-self.min_history_for_optimization*2:-self.min_history_for_optimization]
        
        recent_accuracy = self._calculate_accuracy(recent_batch)
        previous_accuracy = self._calculate_accuracy(previous_batch)
        
        # Trigger optimization if there's a significant drop in accuracy
        if previous_accuracy > 0 and (previous_accuracy - recent_accuracy) / previous_accuracy > self.optimization_threshold:
            logger.warning(f"Performance degradation detected (from {previous_accuracy:.2f} to {recent_accuracy:.2f}). Triggering weight optimization.")
            return True
        return False
    
    def _optimize_weights(self):
        """Optimize model weights based on performance."""
        logger.info("Optimizing model weights based on recent performance.")
        # Placeholder for sophisticated optimization logic.
        # This is where you would adjust self.weight_manager.base_weights
        # based on which models contributed more to correct predictions or errors.
        # For now, it's just a log message.


class SystemHealthAgent:
    def __init__(self):
        self.health_metrics = {
            "memory_usage": [],
            "gpu_utilization": [],
            "model_load_times": {},
            "error_rates": {}
        }
    
    def monitor_system_health(self):
        """Monitor overall system health"""
        self._check_memory_usage()
        self._check_gpu_utilization()
        # You might add _check_model_health() here later
    
    def _check_memory_usage(self):
        """Monitor memory usage"""
        try:
            import psutil
            memory = psutil.virtual_memory()
            self.health_metrics["memory_usage"].append(memory.percent)
            
            if memory.percent > 90:
                logger.warning(f"High memory usage detected: {memory.percent}%")
        except ImportError:
            logger.warning("psutil not installed. Cannot monitor memory usage.")
    
    def _check_gpu_utilization(self):
        """Monitor GPU utilization if available"""
        if torch.cuda.is_available():
            try:
                gpu_util = torch.cuda.memory_allocated() / torch.cuda.max_memory_allocated()
                self.health_metrics["gpu_utilization"].append(gpu_util)
                
                if gpu_util > 0.9:
                    logger.warning(f"High GPU utilization detected: {gpu_util*100:.2f}%")
            except Exception as e:
                logger.warning(f"Error monitoring GPU utilization: {e}")
        else:
            logger.info("CUDA not available. Skipping GPU utilization monitoring.")